DICODerma：一种用于皮肤病学图像元数据管理的实用方法
DICODerma: A practical approach for metadata management of images in dermatology
AI总结：
针对皮肤病成像领域缺乏标准阻碍创新的问题，本研究提出DICODerma实用方案及开源工具，支持将临床皮肤病图像按DICOM标准管理与转换，以低干扰方式推动专科标准 adoption。
摘要：
临床图像对皮肤病的诊断和监测至关重要，且其重要性随着机器学习的日益普及不断提升。与放射学等其他图像密集型专科不同，标准的缺失阻碍了皮肤病成像领域的创新。我们研究了将流行的DICOM（医学数字成像与通信）标准用于皮肤病学图像元数据管理的元需求。我们提出了实用的设计方案，并提供开源工具以将皮肤科医生的工作流与企业成像系统集成。借助该工具，皮肤科医生可对临床图像进行标记、搜索、整理并转换为DICOM格式。我们认为这种低干扰的方法将推动该专科对标准的采用。
英文摘要：
Clinical images are vital for diagnosing and monitoring skin diseases, and their importance has increased with the growing popularity of machine learning. Lack of standards has stifled innovation in dermatological imaging, unlike other image-intensive specialties such as radiology. We investigate the meta-requirements for utilizing the popular DICOM standard for metadata management of images in dermatology. We propose practical design solutions and provide open-source tools to integrate dermatologists' workflow with enterprise imaging systems. Using the tool, dermatologists can tag, search, organize and convert clinical images to the DICOM format. We believe that our less disruptive approach will improve the adoption of standards in the specialty.
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